Convolutional neuronal networks (CNN) have become increasingly popular in the processing of histopathological images [1]}. The main difference with respect to traditional machine learning approaches is that feature extraction is done intrinsically and hierarchically. This allows the network to choose in a an unsupervised fashion the features for optimal cell discrimination. In this work, we present a new CNN-based model for cancer/healthy cell classification on histopathology slides (HPS). We leverage these recent discoveries in deep learning, particularly using the Inception architecture [2]} by combining regularization techniques such as batch normalization [3]} in order to reduce the intrinsic covariate shift.
